Feature Engineering for Predictive Modeling Using Reinforcement Learning

Authors

  • Udayan Khurana IBM Research AI
  • Horst Samulowitz IBM Research AI
  • Deepak Turaga IBM Research AI

Keywords:

Feature Engineering, Predictive Modeling, Supervised Learning, Reinforcement Learning

Abstract

Feature engineering is a crucial step in the process of predictive modeling. It involves the transformation of given feature space, typically using mathematical functions, with the objective of reducing the modeling error for a given target. However, there is no well-defined basis for performing effective feature engineering. It involves domain knowledge, intuition, and most of all, a lengthy process of trial and error. The human attention involved in overseeing this process significantly influences the cost of model generation. We present a new framework to automate feature engineering. It is based on performance driven exploration of a transformation graph, which systematically and compactly captures the space of given options. A highly efficient exploration strategy is derived through reinforcement learning on past examples.

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Published

2018-04-29

How to Cite

Khurana, U., Samulowitz, H., & Turaga, D. (2018). Feature Engineering for Predictive Modeling Using Reinforcement Learning. Proceedings of the AAAI Conference on Artificial Intelligence, 32(1). Retrieved from https://ojs.aaai.org/index.php/AAAI/article/view/11678